Nepali oral microbiomes reflect a gradient of lifestyles from traditional to industrialized

Background Lifestyle plays an important role in shaping the gut microbiome. However, its contributions to the oral microbiome remains less clear, due to the confounding effects of geography and methodology in investigations of populations studied to date. Furthermore, while the oral microbiome seems to differ between foraging and industrialized populations, we lack insight into whether transitions to and away from agrarian lifestyles shape the oral microbiota. Given the growing interest in so-called ‘vanishing microbiomes’ potentially being a risk factor for increased disease prevalence in industrialized populations, it is important that we distinguish lifestyle from geography in the study of microbiomes across populations. Results Here, we investigate salivary microbiomes of 63 Nepali individuals representing a spectrum of lifestyles: foraging, subsistence farming (individuals that transitioned from foraging to farming within the last 50 years), agriculturalists (individuals that have transitioned to farming for at least 300 years), and industrialists (expatriates that immigrated to the United States within the last 20 years). We characterize the role of lifestyle in microbial diversity, identify microbes that differ between lifestyles, and pinpoint specific lifestyle factors that may be contributing to differences in the microbiomes across populations. Contrary to prevailing views, when geography is controlled for, oral microbiome alpha diversity does not differ significantly across lifestyles. Microbiome composition, however, follows the gradient of lifestyles from foraging through agrarianism to industrialism, supporting the notion that lifestyle indeed plays a role in the oral microbiome. Relative abundances of several individual taxa, including Streptobacillus and an unclassified Porphyromonadaceae genus, also mirror lifestyle. Finally, we identify specific lifestyle factors associated with microbiome composition across the gradient of lifestyles, including smoking and grain source. Conclusion Our findings demonstrate that by controlling for geography, we can isolate an important role for lifestyle in determining oral microbiome composition. In doing so, we highlight the potential contributions of several lifestyle factors, underlining the importance of carefully examining the oral microbiome across lifestyles to improve our understanding of global microbiomes.

demultiplexed reads and all the way through DADA2, merging, and chimera removal.

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"Input" column refers to the number of raw reads obtained per sample after sequencing, 1345 "filtered" refers to the number of reads remaining after initial read QC, "denoised" refers 1346 to the number of reads remaining after denoising in DADA2, "nochim" refers to the 1347 number of reads remaining after chimeric sequences were removed, and 1348 "retained_overall" is the total proportion of reads retained following all QC steps from 1349 the input amount.Table does not include the samples that failed to pass initial read QC.

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Title of data: S1 Table -Sequence, survey, population, and questionnaire info of the 1284 sampled individuals 1285 -Description of data: Tab 1 describes survey and sequence metadata data collected.1286 Column abbreviations and responses are explained in greater detail in Tab 3. Column 1287 names that end with "2" contain responses that were categorized and transformed to a 1288 scale of 0-3, in which possible values for binary variables are 0 or 3 (ie.sex) and possible 1289 values for continuous variables are 0, 1, 2, or 3 (ie.fuel source).No survey data was 1290 collected for the American Industrialists.Tab 2 describes the lifestyle pertaining to each 1291 population and their sample sizes.Tab 3 contains the survey questionnaire, including the 1292 codes pertaining to each question asked and list of possible responses.1293 Additional file 3 -S2

1298--factors 1310 --
Kruskal-Wallis module was utilized and p-value correction was applied using the 1299 Benjamini-Hochberg method.Both unadjusted (kw.ep) and adjusted p-values (kw.eBH) 1300 are shown, and adjusted p-value < 0.05 is the threshold for significance.1301 Additional file 4 -S3Table.csv1302 Title of data: S3 Table -Results of genera tested for following the lifestyle gradient.1303 -Description of data: All genera tested for following the lifestyle gradient using the 1304 Jonckheere-Terpstra test followed by the Benjamini-Hochberg method to correct for 1305 multiple tests (BHadj_p_value).Adjusted p-value < 0.05 is the threshold for significance.1306 Nine genera significantly follow the lifestyle gradient.1307 Additional file 5 -S4 Table.csv1308 Title of data: S4 Table -Associations between differentially abundant microbes and1309 lifestyle Description of data: Associations between differentially abundant microbes from the 1311 Jonckheere-Terpstra test and lifestyle factors were tested via linear models.Linear 1312 models were generated between each microbe and each lifestyle factor and then tested for 1313 significance, for a total of 333 tested associations.P-value correction was applied using 1314 the Benjamini-Hochberg method.Both unadjusted and adjusted p-values are shown.1315 Adjusted p-value < 0.05 is the threshold for significance.1316 Additional file 6 -S5 Table.csv1317 Title of data: S5 Table -Predicted functional potential differential abundance results 1318 -Description of data: PICRUSt2 predicted functions were analyzed for differential 1319 abundance based on lifestyle.None of the 107 tested functions were found to be 1320 significant after multiple test correction, but 21/107 pathways were significant prior to 1321 correction.Kruskal-Wallis module in ALDEx2 was utilized and p-value correction was 1322 applied using the Benjamini-Hochberg method.Both unadjusted (kw.ep) and adjusted p-1323 values (kw.eBH) are shown.Adjusted p-value < 0.05 is the threshold for significance.1324 Additional file 7 -S6 Table.csv1325 of data: S6 Table -Samples overlapping between the gut and oral microbiome 1326 studies -Description of List of samples overlapping between the gut and oral microbiome 1328 studies, along with the samples unique to each microbiome study.The first column 1329 "both" lists sample IDs that are associated with both gut and oral samples.The second 1330 column "gut_only" lists sample IDs that are associated with only gut samples.The third 1331 column "oral_only" lists sample IDs that are associated with only oral samples.1332 Additional file 8 -S7 Summary of study findings in Nepali language as translated by Aashish R. 1353 Jha 1354

Table . csv 1333 -
Title of data:

S7 Table -Gut microbiome differential abundance results from 1334 ALDEx2 1335-
Description of data: Gut microbiome genera analyzed for differential abundance based on 1336 lifestyle via ALDEx2.Overall, 37/136 genera were identified as significantly 1337 differentially abundant.Kruskal-Wallis module was utilized and p-value correction was 1338 applied using the Benjamini-Hochberg method.Both unadjusted (kw.ep) and adjusted p-1339 values (kw.eBH) are shown.Adjusted p-value < 0.05 is the threshold for significance.
1341-Title of data: